Arrhythmias

Improvement of electrocardiographic diagnostic accuracy of left ventricular hypertrophy using a Machine Learning approach.

The electrocardiogram (ECG) is the most common tool used to predict left ventricular hypertrophy (LVH). However, it is limited by its low accuracy (<60%) and sensitivity (30%). We set forth the hypothesis that the Machine Learning (ML) C5.0 algorithm could optimize the ECG in the prediction of LVH by echocardiography (Echo) while also establishing ECG-LVH phenotypes. We used Echo as the standard d...

Prediction of mortality from 12-lead electrocardiogram voltage data using a deep neural network.

The electrocardiogram (ECG) is a widely used medical test, consisting of voltage versus time traces collected from surface recordings over the heart. Here we hypothesized that a deep neural network (DNN) can predict an important future clinical event, 1-year all-cause mortality, from ECG voltage-time traces. By using ECGs collected over a 34-year period in a large regional health system, we traine...

Detection of Atrial Fibrillation from Single Lead ECG Signal Using Multirate Cosine Filter Bank and Deep Neural Network.

Atrial fibrillation (AF) is a cardiac arrhythmia which is characterized based on the irregsular beating of atria, resulting in, the abnormal atrial pa...

Deep learning enables automated localization of the metastatic lymph node for thyroid cancer on I post-ablation whole-body planar scans.

The accurate detection of radioactive iodine-avid lymph node (LN) metastasis on I post-ablation whole-body planar scans (RxWBSs) is important in track...

Measurement and identification of mental workload during simulated computer tasks with multimodal methods and machine learning.

This study attempted to multimodally measure mental workload and validate indicators for estimating mental workload. A simulated computer work compose...

Big Data and Atrial Fibrillation: Current Understanding and New Opportunities.

Atrial fibrillation (AF) is the most common arrhythmia with diverse etiology that remarkably relates to high morbidity and mortality. With the advance...

Preliminary clinical application of the robot-assisted CT-guided irreversible electroporation ablation for the treatment of pancreatic head carcinoma.

BACKGROUND: To evaluate the feasibility and safety of a robot-guided irreversible electroporation (IRE) ablation system for the treatment of pancreati...

Multifaceted analysis of training and testing convolutional neural networks for protein secondary structure prediction.

Protein secondary structure prediction remains a vital topic with broad applications. Due to lack of a widely accepted standard in secondary structure...

Analysis of Drug Effects on iPSC Cardiomyocytes with Machine Learning.

Patient-specific induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) offer an attractive experimental platform to investigate cardiac dise...

Recognition of Patient Groups with Sleep Related Disorders using Bio-signal Processing and Deep Learning.

Accurately diagnosing sleep disorders is essential for clinical assessments and treatments. Polysomnography (PSG) has long been used for detection of ...

Deep learning-based monocular placental pose estimation: towards collaborative robotics in fetoscopy.

PURPOSE: Twin-to-twin transfusion syndrome (TTTS) is a placental defect occurring in monochorionic twin pregnancies. It is associated with high risks ...

Application of a convolutional neural network for predicting the occurrence of ventricular tachyarrhythmia using heart rate variability features.

Predicting the occurrence of ventricular tachyarrhythmia (VTA) in advance is a matter of utmost importance for saving the lives of cardiac arrhythmia ...

Contactless Real-Time Heartbeat Detection via 24 GHz Continuous-Wave Doppler Radar Using Artificial Neural Networks.

The measurement of human vital signs is a highly important task in a variety of environments and applications. Most notably, the electrocardiogram (EC...

Deep Multi-Scale Fusion Neural Network for Multi-Class Arrhythmia Detection.

Automated electrocardiogram (ECG) analysis for arrhythmia detection plays a critical role in early prevention and diagnosis of cardiovascular diseases...

Detection of Atrial Fibrillation Using 1D Convolutional Neural Network.

The automatic detection of atrial fibrillation (AF) is crucial for its association with the risk of embolic stroke. Most of the existing AF detection ...

Automatic diagnosis of the 12-lead ECG using a deep neural network.

The role of automatic electrocardiogram (ECG) analysis in clinical practice is limited by the accuracy of existing models. Deep Neural Networks (DNNs)...

Application of deep learning techniques for heartbeats detection using ECG signals-analysis and review.

Deep learning models have become a popular mode to classify electrocardiogram (ECG) data. Investigators have used a variety of deep learning technique...

Top-down machine learning approach for high-throughput single-molecule analysis.

Single-molecule approaches provide enormous insight into the dynamics of biomolecules, but adequately sampling distributions of states and events ofte...

End-to-End Deep Learning Fusion of Fingerprint and Electrocardiogram Signals for Presentation Attack Detection.

Although fingerprint-based systems are the commonly used biometric systems, they suffer from a critical vulnerability to a presentation attack (PA). T...

Cloud-based ECG monitoring using event-driven ECG acquisition and machine learning techniques.

An approach is proposed for the detection of chronic heart disorders from the electrocardiogram (ECG) signals. It utilizes an intelligent event-driven...